This work presents a method to identify linear and nonlinear loads commonly encountered in residential electrical systems. From this method, feasible solutions can be applied to mitigate the high levels of harmonic currents, generated mainly by nonlinear loads. Techniques of feature selection were used to data preprocessing and to minimize the effort in identification of loads connected to the electrical system. For the next step, the load identification, artificial neural networks were applied. All harmonic distortion situations were created in laboratory from a power source, and in its outputs were inserted the loads and power quality analyzers, which perform the extraction of all measurements. The obtained results were considered satisfactory, which show that the methodology can be employed by power distribution companies in order to obtain information about the profile of loads used by residential consumers.
Identification of linear and nonlinear loads; harmonic components; artificial neural networks
